The ensemble Kalman filter (EnKF) has recently received significant attention as a robust and efficient tool for data assimilation. Although the EnKF has many advantages such as ease of implementation and efficient uncertainty quantification, it suffers from a few key issues that limit the application of the EnKF to large-scale simulation models of real fields. Among these various issues, one of the important ones is the well known problem of ensemble collapse, which is particularly evident for small ensembles. The problem arises because all the ensemble members are driven in more or less the same direction through each ensemble update. This implies that as the members are updated again and again, they will ultimately converge towards the same ensemble member. This results in an artificial reduction of variability across the ensemble, resulting in a poor quantification of uncertainty. The second, more important problem is that the EnKF is theoretically appropriate only if all ensemble members belong to the same multi-Gaussian random field (geological/geostatistical model). This is because the updated ensemble members are a linear combination of the forecasted ensemble members. If each ensemble member has different geostatistical properties, the updated ensemble members will not be able to honor these geostatistical properties. Ultimately, all the updated ensemble members will have similar geostatistical properties. This is an important problem because there is more than one geological scenario for most real fields, and it is desired to obtain one or more history-matched models for each geological scenario. One way to resolve this problem is to apply a different EnKF for each geological scenario. This however, is not very efficient as this increases the computational burden N times over the standard EnKF for N geological scenarios. For example, if each EnKF has 100 ensemble members (a typical number), and there are 10 geological scenarios, this approach would require 1000 simulations, which may not be practical for large-scale models.